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Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features.

, , , , and . Comput. Biol. Medicine, (2019)

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Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads., , , , , , , , , and 2 other author(s). Knowl. Based Syst., (2016)Automated detection of shockable ECG signals: A review., , , , , , , , , and 5 other author(s). Inf. Sci., (2021)A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank., , and . Comput. Biol. Medicine, (2018)Deep Image Analysis for Microalgae Identification., , , , , , , , , and . iiWAS, volume 14416 of Lecture Notes in Computer Science, page 280-292. Springer, (2023)Automatic Generation of Surface Meshes for Right Ventricle with 1-to-1 Vertex Correspondence from Cine-MR Images., , , , and . CinC, page 753-756. www.cinc.org, (2015)Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM., , , , and . Comput. Biol. Medicine, (2020)Automatic Segmentation of Coronary Artery Lumen via Anisotropic Graph-cuts., , , , , , and . EMBC, page 4871-4874. IEEE, (2019)Feature Tracking for Ventricular Strain Assessment in Heart Failure with Preserved Ejection Fraction., , , and . CinC, page 1-2. IEEE, (2019)Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images., , , , , , , , , and . J. Digit. Imaging, 36 (3): 879-892 (June 2023)VEntNet: Hybrid deep convolutional neural network model for automated multi-class categorization of chest X-rays., , , , and . Int. J. Imaging Syst. Technol., 32 (3): 778-797 (2022)